Solving Large-Scale Function Optimization Problem by Using a New Metaheuristic Algorithm Based on Quantum Dolphin Swarm Algorithm

Meta-heuristic algorithm has been a research hotspot in solving the optimal solution of large-scale functions. However, meta-heuristic algorithms are prone to fall into local optimum problems, such as the recently proposed dolphin swarm algorithm (DSA). To solve this problem, in this study, the quan...

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Vydáno v:IEEE access Ročník 7; s. 138972 - 138989
Hlavní autoři: Qiao, Weibiao, Yang, Zhe
Médium: Journal Article
Jazyk:angličtina
Vydáno: Piscataway IEEE 2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Algorithms
dolphin swarm algorithm
Dolphins
Heuristic
Heuristic algorithms
Heuristic methods
large-scale function optimization
Mathematical model
Optimization
Particle swarm optimization
Quantum computing
Quantum search algorithm
Search algorithms
Search problems
Whales
ISSN:2169-3536, 2169-3536
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Shrnutí:Meta-heuristic algorithm has been a research hotspot in solving the optimal solution of large-scale functions. However, meta-heuristic algorithms are prone to fall into local optimum problems, such as the recently proposed dolphin swarm algorithm (DSA). To solve this problem, in this study, the quantum search algorithm is introduced into DSA. In addition, to test the performance of the proposed quantum dolphin swarm algorithm (QDSA), six commonly used large-scale functions (e.g. Rotated hyper-ellipsoid function) are taken as examples. Furthermore, some advanced algorithms (e.g. whale optimization algorithm (WOA)) are used for comparison. The results show that the ability of QDSA to obtain global optimal solution is obviously improved compared with DSA, and the performance of QDSA is superior to other algorithms considered for comparison. Finally, it can be concluded that such a novel meta-heuristic algorithm may help to improve the problem of solving the optimal solution of large-scale functions.
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ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2019.2942169